Today's automotive engine control system consists of engine sensors and actuators wired directly to an electronic control unit (ECU) or controller that contains embedded software. The complexity of the software continues to increase as the number of sensors and actuators used on the vehicle increases. Under current governmental regulations, each emission related sensor must be diagnosed such that faulty sensors are replaced as soon as possible to minimize pollution and poor vehicle performance. For this reason, each sensor is monitored by a diagnostic algorithm that is responsible for detecting sensor values that are irrational or out of range. There are countless ways that a diagnostic may differentiate a good sensor from a bad one, leading to an unwieldy diagnostic development process with many analysis options.
The current process of creating a diagnostic for sensors involves designing the algorithm, comparing data collected from a good sensor with that of a bad sensor and trying to determine the values for differentiating a good system from a bad one. The process of producing the parameters that differentiate a good sensor from a bad one for the various operating environments will be referred to as the diagnostic development process. This process is typically done manually by a person using trial and error to determine the differences in the system when sensors are good vs. when they are faulty. Decisions are based on data taken from different environments, which include different climates, altitudes, and vehicle operating conditions. Present diagnostic development processes are time consuming and rely heavily on heuristic data.